Adaptive Deep Learning-Based Intrusion Detection System for Intelligent IoT Communication Environments

Abstract

With the advent of the Internet of Things (IoT) technologies, smart devices, industrial automation, healthcare, transportation, and smart city environments are now becoming intelligent and connected. The diversity and limited resources of IoT networks, however, make them particularly susceptible to new cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Spoofing, Ransomware and Unauthorized Access. Traditional IDSs are mostly based on static signature-based techniques and centralized monitoring systems, which are not effective against dynamic and complex attack patterns in real time. In addition, at the scale of IoT, the communication environments need adaptive, scalable, and intelligent cybersecurity solutions that can help maintain secure and resilient network operation. The paper presents an Intrusion Detection System (IDS) using Adaptive Deep Learning for intelligent IoT communication environment. The framework proposed connects the elements of distributed traffic monitoring, edge-assisted security analysis, adaptive threat classification, and Explainable Artificial Intelligence (XAI) mechanisms in the context of intelligent cyber defense. A novel hybrid Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU) architecture is designed to effectively capture traffic patterns and effectively detect evolving cyber threat. The framework performs packet-level communication analysis, protocol behavior analysis, flow analysis and anomaly pattern analysis in heterogeneous IoT infrastructures continuously. Furthermore, explainability modules offer clear explanations of intrusion decisions, pinpointing the significant features that have influenced the results related to intrusion classification. The experimental results validate that the proposed framework outperforms the traditional cybersecurity framework in terms of intrusion detection accuracy, false alarm rate, detection latency and adaptability. The proposed solution helps build secure, scalable and sustainable communication infrastructures for the IoT by implementing intelligent threat monitoring and adaptive cyber defense.